:albemic: | Experimenting with other model layouts.
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416500f7fc
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26
data.py
26
data.py
@ -4,23 +4,20 @@ import torch
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import torchaudio
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import os
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import random
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import torchaudio.transforms as T
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import AudioUtils
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class AudioDataset(Dataset):
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#audio_sample_rates = [8000, 11025, 16000, 22050]
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audio_sample_rates = [11025]
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MAX_LENGTH = 88200 # Define your desired maximum length here
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def __init__(self, input_dir, device):
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self.input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav')]
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self.device = device
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def __len__(self):
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return len(self.input_files)
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def __getitem__(self, idx):
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# Load high-quality audio
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high_quality_audio, original_sample_rate = torchaudio.load(self.input_files[idx], normalize=True)
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@ -33,7 +30,24 @@ class AudioDataset(Dataset):
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resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
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low_quality_audio = resample_transform_high(low_quality_audio)
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high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio).to(self.device)
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low_quality_audio = AudioUtils.stereo_tensor_to_mono(low_quality_audio).to(self.device)
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high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_audio)
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low_quality_audio = AudioUtils.stereo_tensor_to_mono(low_quality_audio)
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# Pad or truncate high-quality audio
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if high_quality_audio.shape[1] < self.MAX_LENGTH:
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padding = self.MAX_LENGTH - high_quality_audio.shape[1]
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high_quality_audio = F.pad(high_quality_audio, (0, padding))
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elif high_quality_audio.shape[1] > self.MAX_LENGTH:
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high_quality_audio = high_quality_audio[:, :self.MAX_LENGTH]
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# Pad or truncate low-quality audio
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if low_quality_audio.shape[1] < self.MAX_LENGTH:
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padding = self.MAX_LENGTH - low_quality_audio.shape[1]
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low_quality_audio = F.pad(low_quality_audio, (0, padding))
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elif low_quality_audio.shape[1] > self.MAX_LENGTH:
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low_quality_audio = low_quality_audio[:, :self.MAX_LENGTH]
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high_quality_audio = high_quality_audio.to(self.device)
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low_quality_audio = low_quality_audio.to(self.device)
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return (high_quality_audio, original_sample_rate), (low_quality_audio, mangled_sample_rate)
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@ -2,35 +2,54 @@ import torch
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import torch.nn as nn
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import torch.nn.utils as utils
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def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
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def discriminator_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, spectral_norm=True):
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padding = (kernel_size // 2) * dilation
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conv_layer = nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)
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if spectral_norm:
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conv_layer = utils.spectral_norm(conv_layer)
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return nn.Sequential(
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utils.spectral_norm(nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)),
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conv_layer,
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nn.LeakyReLU(0.2, inplace=True),
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nn.BatchNorm1d(out_channels)
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)
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class SISUDiscriminator(nn.Module):
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def __init__(self):
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super(SISUDiscriminator, self).__init__()
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layers = 4 # Increased base layer count
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self.model = nn.Sequential(
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discriminator_block(1, layers, kernel_size=7, stride=2), # Initial downsampling
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discriminator_block(layers, layers * 2, kernel_size=5, stride=2), # Downsampling
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discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2), # Increased dilation
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discriminator_block(layers * 4, layers * 4, kernel_size=5, dilation=4), # Increased dilation
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discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=8), # Deeper layer!
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discriminator_block(layers * 8, layers * 8, kernel_size=5, dilation=1), # Deeper layer!
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discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=2), # Reduced dilation
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discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=1),
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discriminator_block(layers * 2, layers, kernel_size=3, stride=1), # Final convolution
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discriminator_block(layers, 1, kernel_size=3, stride=1)
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class AttentionBlock(nn.Module):
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def __init__(self, channels):
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super(AttentionBlock, self).__init__()
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self.attention = nn.Sequential(
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nn.Conv1d(channels, channels // 4, kernel_size=1),
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nn.ReLU(),
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nn.Conv1d(channels // 4, channels, kernel_size=1),
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nn.Sigmoid()
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)
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self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
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def forward(self, x):
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# Gaussian noise is not necessary here for discriminator as it is already implicit in the training process
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attention_weights = self.attention(x)
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return x * attention_weights
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class SISUDiscriminator(nn.Module):
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def __init__(self, layers=4): #Increased base layer count
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super(SISUDiscriminator, self).__init__()
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self.model = nn.Sequential(
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discriminator_block(1, layers, kernel_size=7, stride=4), #Aggressive downsampling
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discriminator_block(layers, layers * 2, kernel_size=5, stride=2),
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discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=2),
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discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=4),
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AttentionBlock(layers * 8), #Added attention
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discriminator_block(layers * 8, layers * 16, kernel_size=5, dilation=8),
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discriminator_block(layers * 16, layers * 16, kernel_size=3, dilation=1),
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discriminator_block(layers * 16, layers * 8, kernel_size=3, dilation=2),
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discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=1),
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discriminator_block(layers * 4, layers * 2, kernel_size=3, stride=1),
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discriminator_block(layers * 2, layers, kernel_size=3, stride=1),
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discriminator_block(layers, 1, kernel_size=3, stride=1, spectral_norm=False) #last layer no spectral norm.
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)
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self.global_avg_pool = nn.AdaptiveAvgPool1d(1)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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x = self.model(x)
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x = self.global_avg_pool(x)
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x = x.view(-1, 1)
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x = self.sigmoid(x)
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return x
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44
generator.py
44
generator.py
@ -7,30 +7,46 @@ def conv_block(in_channels, out_channels, kernel_size=3, dilation=1):
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nn.PReLU()
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)
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class AttentionBlock(nn.Module):
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def __init__(self, channels):
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super(AttentionBlock, self).__init__()
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self.attention = nn.Sequential(
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nn.Conv1d(channels, channels // 4, kernel_size=1),
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nn.ReLU(),
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nn.Conv1d(channels // 4, channels, kernel_size=1),
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nn.Sigmoid()
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)
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def forward(self, x):
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attention_weights = self.attention(x)
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return x * attention_weights
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class ResidualInResidualBlock(nn.Module):
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def __init__(self, channels, num_convs=3):
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super(ResidualInResidualBlock, self).__init__()
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self.conv_layers = nn.Sequential(*[conv_block(channels, channels) for _ in range(num_convs)])
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self.attention = AttentionBlock(channels)
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def forward(self, x):
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residual = x
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x = self.conv_layers(x)
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x = self.attention(x)
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return x + residual
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class SISUGenerator(nn.Module):
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def __init__(self):
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def __init__(self, layer=4, num_rirb=4): #increased base layer and rirb amounts
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super(SISUGenerator, self).__init__()
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layer = 4 # Increased base layer count
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self.conv1 = nn.Sequential(
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nn.Conv1d(1, layer, kernel_size=7, padding=3),
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nn.BatchNorm1d(layer),
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nn.PReLU(),
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)
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self.conv_blocks = nn.Sequential(
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conv_block(layer, layer, kernel_size=3, dilation=1), # Local details
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conv_block(layer, layer*2, kernel_size=5, dilation=2), # Local Context
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conv_block(layer*2, layer*2, kernel_size=3, dilation=16), # Longer range dependencies
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conv_block(layer*2, layer*2, kernel_size=5, dilation=8), # Wider context
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conv_block(layer*2, layer, kernel_size=5, dilation=2), # Local Context
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conv_block(layer, layer, kernel_size=3, dilation=1), # Local details
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)
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self.final_layer = nn.Sequential(
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nn.Conv1d(layer, 1, kernel_size=3, padding=1),
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)
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self.rir_blocks = nn.Sequential(*[ResidualInResidualBlock(layer) for _ in range(num_rirb)])
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self.final_layer = nn.Conv1d(layer, 1, kernel_size=3, padding=1)
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def forward(self, x):
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residual = x
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x = self.conv1(x)
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x = self.conv_blocks(x)
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x = self.rir_blocks(x)
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x = self.final_layer(x)
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return x + residual
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27
training.py
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training.py
@ -38,7 +38,7 @@ device = torch.device(args.device if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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mfcc_transform = T.MFCC(
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sample_rate=16000, # Adjust to your sample rate
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sample_rate=44100, # Adjust to your sample rate
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n_mfcc=20,
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melkwargs={'n_fft': 2048, 'hop_length': 512} # adjust n_fft and hop_length to your needs.
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).to(device)
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@ -97,20 +97,9 @@ debug = args.verbose
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dataset_dir = './dataset/good'
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dataset = AudioDataset(dataset_dir, device)
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# ========= MULTIPLE =========
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# dataset_size = len(dataset)
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# train_size = int(dataset_size * .9)
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# val_size = int(dataset_size-train_size)
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#train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
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# train_data_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)
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# val_data_loader = DataLoader(val_dataset, batch_size=1, shuffle=True)
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# ========= SINGLE =========
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train_data_loader = DataLoader(dataset, batch_size=1, shuffle=True)
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train_data_loader = DataLoader(dataset, batch_size=128, shuffle=True)
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# Initialize models and move them to device
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generator = SISUGenerator()
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@ -175,17 +164,17 @@ def start_training():
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scheduler_g.step(combined_loss)
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# ========= SAVE LATEST AUDIO =========
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high_quality_audio = high_quality_clip
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low_quality_audio = low_quality_clip
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ai_enhanced_audio = (generator_output, high_quality_clip[1])
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high_quality_audio = (high_quality_clip[0][0], high_quality_clip[1][0])
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low_quality_audio = (low_quality_clip[0][0], low_quality_clip[1][0])
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ai_enhanced_audio = (generator_output[0], high_quality_clip[1][0])
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new_epoch = generator_epoch+epoch
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if generator_epoch % 10 == 0:
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print(f"Saved epoch {new_epoch}!")
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[0][0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0][0].cpu(), ai_enhanced_audio[1])
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0][0].cpu(), high_quality_audio[1])
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-crap.wav", low_quality_audio[0].cpu(), high_quality_audio[1]) # <-- Because audio clip was resampled in data.py from original to crap and to original again.
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-ai.wav", ai_enhanced_audio[0].cpu(), ai_enhanced_audio[1])
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torchaudio.save(f"./output/epoch-{new_epoch}-audio-orig.wav", high_quality_audio[0].cpu(), high_quality_audio[1])
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if debug:
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print(generator.state_dict().keys())
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